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Open-Source Databases · apache

doris

Apache Doris is an open-source, real-time analytics and search database designed for AI agents, supporting hybrid queries across structured, text, and vector data. It operates on an MPP (massively parallel processing) architecture and can run in compute-storage coupled or decoupled modes, making it suitable for data warehousing, observability, and customer-facing analytics at scale.

Source: GitHub — github.com/apache/doris
15.6k
GitHub stars
3.9k
Forks
Java
Primary language
Apache-2.0
License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

FieldValue
Repositoryapache/doris
Ownerapache
Primary languageJava
LicenseApache-2.0 — OSI-approved
Stars15.6k
Forks3.9k
Open issues1.1k
Latest release4.1.2 (2026-06-17)
Last updated2026-07-08
Sourcehttps://github.com/apache/doris

What doris is

Doris is a Java-based OLAP database with MPP architecture that ingests streaming data, accelerates lakehouse queries over Iceberg/Delta/Hudi, and executes SQL-native hybrid search across JSON, full-text, and vector indexes. It provides multiple deployment modes, connectors for Flink/Spark/Kafka, and stateless compute groups for on-demand scaling.

Quickstart

Get the doris source

Clone the repository and explore it locally.

terminalbash
git clone https://github.com/apache/doris.gitcd doris# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Real-Time Analytics & Observability

Ingest high-throughput logs, events, and metrics with sub-second query latency under concurrent load. Well-suited for monitoring, alerting, and operational dashboards.

Data Warehousing & Lakehouse Acceleration

Build unified real-time warehouse across business domains; query open table formats (Iceberg, Delta, Hudi) without data duplication. Reduces ETL complexity and improves time-to-insight.

Hybrid Search for AI & Search Workloads

Execute SQL queries that combine vector, full-text, and structured search in a single engine. Eliminates need for separate vector DB and search index systems for AI-driven applications.

Implementation considerations

  • Cluster setup and configuration require distributed systems knowledge; plan for network topology, resource sizing, and HA deployment modes (coupled vs. decoupled).
  • Ingestion latency and consistency guarantees depend on connector choice (Flink, Spark, Kafka) and configuration; validate against your SLA before committing to production.
  • Vector indexing and hybrid search features are present but their performance and cost-efficiency at scale should be benchmarked against your data volume and query patterns.
  • Java-based codebase; operators must be comfortable with JVM tuning, garbage collection, and Java troubleshooting.
  • Schema evolution and DDL operations can impact running queries; plan downtime windows or use compute-storage decoupled mode to minimize user-facing impact.

When to avoid it — and what to weigh

  • Transactional OLTP with Complex Joins — Doris is optimized for analytics, not row-level transactional updates. If your use case requires sub-millisecond row mutations or complex multi-table ACID transactions, consider OLTP databases.
  • Minimal Operational Capacity — Requires cluster management, distributed configuration, and monitoring expertise. Not suitable if you need a zero-ops embedded database or fully managed SaaS without infrastructure responsibility.
  • Simple Relational Queries Only — If your workload is basic SQL queries on small datasets without analytics, hybrid search, or high concurrency needs, simpler databases may be more cost-effective and easier to operate.
  • Proprietary or Proprietary-Lock-In Requirements — Doris is Apache 2.0 open-source with no commercial license tier or vendor lock-in protection. Organizations requiring vendor support contracts or proprietary feature parity should evaluate commercial alternatives.

License & commercial use

Apache Doris is licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license. Code can be freely used, modified, and distributed in open-source and commercial products, subject to license and copyright notice requirements.

Apache 2.0 permits commercial use without license fees or vendor permission. However, there is no commercial support, warranty, or SLA from the Apache project itself. Enterprise users should evaluate community support (GitHub Issues, Slack, discussions) and assess hiring or consulting services to manage production deployments. Use of the "Apache Doris" trademark is governed separately by the Apache Software Foundation.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

SignalAssessment
MaintenanceActive
DocumentationStrong
License clarityClear
Deployment complexityHigh
DEV.co fitGood
Assessment confidenceHigh
Security considerations

Open-source codebase subject to public audit and community patches. No security posture claims made in the data. Standard distributed database concerns apply: encryption at rest and in transit, authentication/RBAC, data isolation in multi-tenant scenarios. Operators must conduct their own security review, apply timely updates, and harden network/cluster configuration. No independent security audit or certification mentioned.

Alternatives to consider

ClickHouse

Lightweight, high-performance columnar OLAP database; simpler deployment for time-series and analytics. Lacks hybrid search and lakehouse integration; smaller ecosystem than Doris.

Snowflake

Fully managed, cloud-native data warehouse with native vector search support. Commercial offering with SLA and vendor support; eliminates operational overhead but locks into proprietary platform and pricing.

Elasticsearch + PostgreSQL

Elasticsearch for full-text and vector search, PostgreSQL for structured data and transactions. Mature ecosystems and simpler ops for small teams; requires dual-system management and cross-system query coordination.

Software development agency

Build on doris with DEV.co software developers

Start with the quick-start guide and sandbox deployment. For production use, assess operational capacity, benchmark against your queries, and validate ecosystem integrations (Flink, Spark, BI tools). Contact our team to discuss architecture fit and implementation strategy.

Talk to DEV.co

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doris FAQ

Does Doris support transactions and ACID guarantees?
Doris is an OLAP database optimized for analytics, not OLTP. It supports eventual consistency and bulk mutations, not row-level ACID transactions. Not suitable for systems requiring strict transaction isolation.
Can I run Doris without a distributed cluster?
Doris is designed for distributed MPP operation. Single-node deployments exist for development/testing but are not recommended for production. Compute-storage decoupled mode simplifies scaling but still requires cluster management.
What happens when I query external lakehouse formats (Iceberg, Delta)?
Doris can query open table formats directly without copying data. Performance depends on object store latency and format complexity. Always benchmark against your data volume and query patterns before production use.
Is there commercial support available?
Apache Doris itself is community-supported via GitHub Issues, Slack, and discussions. No official commercial support, SLA, or enterprise license from Apache. Operators may hire consultants or form dedicated teams.

Software developers & web developers for hire

DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If doris is part of your open-source databases roadmap, our team can implement, customize, migrate, and maintain it.

Ready to Evaluate Apache Doris?

Start with the quick-start guide and sandbox deployment. For production use, assess operational capacity, benchmark against your queries, and validate ecosystem integrations (Flink, Spark, BI tools). Contact our team to discuss architecture fit and implementation strategy.